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IEEE CIM Special Issue on "Knowledge Transfer in Evolutionary Optimization," Guest Editors: Liang Feng, Handing Wang, Yaochu Jin, Kay Chen Tan. Submission Deadline: February 1, 2021. (Call for Papers)
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Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem
Authors: Michalis Mavrovouniotis, Shengxiang Yang, Mien Van, Changhe Li, Marios Polycarpou
Publication: IEEE Computational Intelligence Magazine (CIM)
Issue: Volume 15, Issue 1 – February 2020
Abstract: Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.
Index Terms: Heuristic algorithms, Ant colony optimization, Classification algorithms, Particle swarm optimization, Benchmark testing
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8957215